The growth in global population inevitably increases the consumption of natural resources. The need to provide basic services to these growing communities leads to an increase in anthropogenic changes to the natural environment. The resulting transformation of vegetation cover (e.g. deforestation, agricultural expansion, urbanisation) has significant impacts on hydrology, biodiversity, ecosystems and climate. Human settlement expansion is the most common driver of land cover change in South Africa, and is currently mapped on an irregular, ad hoc basis using visual interpretation of aerial photographs or satellite images. This thesis proposes several methods of detecting newly formed human settlements using hyper-temporal, multi-spectral, medium spatial resolution MODIS land surface reflectance satellite imagery. The hyper-temporal images are used to extract time series, which are analysed in an automated fashion using machine learning methods. A post-classification change detection framework was developed to analyse the time series using several feature extraction methods and classifiers. Two novel hyper-temporal feature extraction methods are proposed to characterise the seasonal pattern in the time series. The first feature extraction method extracts Seasonal Fourier features that exploits the difference in temporal spectra inherent to land cover classes. The second feature extraction method extracts state-space vectors derived using an extended Kalman filter. The extended Kalman filter is optimised using a novel criterion which exploits the information inherent in the spatio-temporal domain. The post-classification change detection framework was evaluated on different classifiers; both supervised and unsupervised methods were explored. A change detection accuracy of above 85% with false alarm rate below 10% was attained. The best performing methods were then applied at a provincial scale in the Gauteng and Limpopo provinces to produce regional change maps, indicating settlement expansion. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Space travelers in science fiction can drop out of hyperspace and make a pinpoint landing on any strange new world without stopping to get their bearings, but real-life space navigation is an art characterized by limited information and complex mathematics that yield no easy answers. This study investigates, for the first time ever, what position and velocity estimation errors can be expected by a starship arriving at a distant star - specifically, a miniature probe like those proposed by the Breakthrough Starshot initiative arriving at Proxima Centauri. Such a probe consists of nothing but a small optical camera and a small microprocessor, and must therefore rely on relatively simple methods to determine its position and velocity, such as observing the angles between its destination and certain guide stars and processing them in an algorithm known as an extended Kalman filter. However, this algorithm is designed for scenarios in which the position and velocity are already known to high accuracy. This study shows that the extended Kalman filter can reliably estimate the position and velocity of the Starshot probe at speeds characteristic of current space probes, but does not attempt to model the filter’s performance at speeds characteristic of Starshot-style proposals. The gravity of the target star is also estimated using the same methods.

No description available.

This thesis considers the problem of using signals of opportunity (SOO) with known direction of arrival (DOA) for aircraft positioning. SOO is a collective name for a wide range of signals not intended for navigation but which can be intercepted by the radar warning system on an aircraft. These signals can for example aid an unassisted inertial navigation system (INS) in areas where the global navigation satellite system (GNSS) is inaccessible. Challenges arise as the signals are transmitted from non-controllable sources without any guarantee of quality and availability. Hence, it is important that any estimation method utilising SOO is robust and statistically consistent in case of time-varying signals of different quality, missed detections and unreliable signals such as outliers. The problem is studied using SOO sources with either known or unknown locations. An extended Kalman filter (EKF) based solution is proposed for the first case which is shown to significantly improve the localisation performance compared to an unassisted INS in common scenarios. Yet, a number of factors affect this performance, including the measurement noise variance, the signal rate and the availability of known source locations. An outlier rejection mechanism is developed which is shown to increase the robustness of the suggested method. A numerical evaluation indicates that statistical consistency can be maintained in many situations even with the above-mentioned challenges. An EKF based simultaneous localisation and mapping (SLAM) solution is proposed for the case with unknown SOO source locations. The flight trajectory and initialisation process of new SOO sources are critical in this case. A method based on nonlinear least squares is proposed for the initialisation process, where new SOO sources are only allowed to be initialised in the filter once a set of requirements are fulfilled. This method has shown to increase the robustness during initialisation, when the outlier rejection is not applicable. When combining known and unknown SOO source locations, a more stable localisation solution is obtained compared to when all locations are unknown. Applicability of the proposed solution is verified by a numerical evaluation. The computational time increases cubically with the number of sources in the state and quadratically with the number of measurements. The time is substantially increased during landmark initialisation.

Monocular cameras are prominently used for estimating motion of Unmanned Aerial Vehicles. With growing interest in autonomous vehicle technology, the use of monocular cameras in ground vehicles is on the rise. This is especially favorable for localization in situations where Global Navigation Satellite System (GNSS) is unreliable, such as open-pit mining environments. However, most monocular camera based approaches suffer due to obscure scale information. Ground vehicles impose a greater difficulty due to high speeds and fast movements. This thesis aims to estimate the scale of monocular vision data by using an inertial sensor in addition to the camera. It is shown that the simultaneous estimation of pose and scale in autonomous ground vehicles is possible by the fusion of visual and inertial sensors in an Extended Kalman Filter (EKF) framework. However, the convergence of scale is sensitive to several factors including the initialization error. An accurate estimation of scale allows the accurate estimation of pose. This facilitates the localization of ground vehicles in the absence of GNSS, providing a reliable fall-back option. / Monokulära kameror används ofta vid rörelseestimering av obemannade flygande farkoster. Med det ökade intresset för autonoma fordon har även användningen av monokulära kameror i fordon ökat. Detta är fram för allt fördelaktigt i situationer där satellitnavigering (Global Navigation Satellite System (GNSS)) äropålitlig, exempelvis i dagbrott. De flesta system som använder sig av monokulära kameror har problem med att estimera skalan. Denna estimering blir ännu svårare på grund av ett fordons större hastigheter och snabbare rörelser. Syftet med detta exjobb är att försöka estimera skalan baserat på bild data från en monokulär kamera, genom att komplettera med data från tröghetssensorer. Det visas att simultan estimering av position och skala för ett fordon är möjligt genom fusion av bild- och tröghetsdata från sensorer med hjälp av ett utökat Kalmanfilter (EKF). Estimeringens konvergens beror på flera faktorer, inklusive initialiseringsfel. En noggrann estimering av skalan möjliggör också en noggrann estimering av positionen. Detta möjliggör lokalisering av fordon vid avsaknad av GNSS och erbjuder därmed en ökad redundans.

The most common method for securing components to each other during manufacturing of products is by joining these using screws, nuts and bolts. The benefit of using this method is that it is cheap and makes it easy to join and separate components quickly. The clamping force in the threaded joint is critical to the quality and in some respect the life length of the product, which makes it important to have good control of the clamping force. There are two main tightening strategies used when tightening a threaded joint – torque controlled tightening and angle controlled tightening. The first method monitors the applied torque during the entire tightening and halts when the target torque is reached. The second method, angle controlled tightening, measures the rotation of the threaded fastener in the joint. This method generally produces more accurate results with less scatter in the final clamping force. In order to apply angle controlled tightening using a hand-held tool it is required to not only control the output angle of the tool, but also how the tool moves in relation to the joint. This is to ensure that the control signal from the motor actually translates to clamping force in the joint and not to rotation of the tool itself. This thesis project aims to analyze data from an IMU (Inertial Measurement Unit) built into a hand-held tightening tool in order to estimate tool movement and thereby react to undesired tool movement. An analysis has been performed to evaluate how the two sensor fusion methods – Kalman filter and Particle filter – perform in terms of estimating the orientation of the tool by combining measurements from the IMU’s accelerometers and gyroscopes. Data was collected from the tool IMU during a number of angle tightening sequences with varying setups. Test were performed both for when the tool was kept still during the entire tightening and for when the tools was allowed to move freely. Tests were also carried out for a couple of different tool orientations to better understand the behavior of the two sensor fusion models. The results from the tests showed that the Kalman Filter was able to better estimate the tool orientation. Especially in terms of accuracy, repeatability and reliability. / Den vanligaste metoden för att fästa komponenter till varandra vid tillverkning av produkter är genom att sammanfoga komponenterna med hjälp av skruvar, bultar och muttrar. Fördelen med denna metod är att den är billig och gör det enkelt att sammanfoga och lossa komponenter snabbt. Klämkraften i skruvförbandet är avgörande för hur väl en produkt är ihopsatt och påverkar därmed dess kvalitet, samt i viss mån livslängd. Det finns i huvudsak två olika strategier vid åtdragning i ett skruvförband – momentåtdragning och vinkelåtdragning. Den första metoden bygger på att man kontinuerligt mäter momentet under åtdragning och avbryter åtdragningen när rätt moment uppnåtts. Den andra metoden, vinkelåtdragning, mäter hur många grader fästelementet roterat i förbandet. Metoden producerar i regel högre precision med mindre spridning av den slutgiltiga klämraften. För att kunna tillämpa vinkelåtdragning med ett handhållet verktyg räcker det inte att kontroll över rotationen av verktygets utgående axel, utan även hur verktyget rör sig i förhållande till förbandet under åtdragning. Detta för att säkerställa att verktygets motorstyrning resulterar i önskad klämkraft i förbandet och inte rotation av själva verktyget. Detta examensarbete ämnar analysera data från en IMU (Inertial Measurement Unit) integrerad i ett handhållet åtdragningsverktyg för att estimera verktygets rörelse under vinkelåtdragning och därmed kompensera för oönskade rörelser. En analys har gjorts för hur väl de två olika sensorfusions-modellerna - Kalmanfilter och Partikelfilter – presterar när det kommer till att uppskatta orientering för verktyget genom att kombinera data från IMU-enhetens accelerometrar och gyroskop. Data samlades in från verktygets IMU från ett antal dragningar med varierande uppställning. Tester genomfördes dels då verktyget hölls stilla under hela åtdragningen och dels då det tilläts röra sig fritt. Tester genomfördes även för flera olika orienteringar av verktyget för att i större utsträckning kunna säga hur de olika sensorfusions-modellerna presterade. Resultatet av testerna visade att Kalmanfiltret kunde producera bättre estimeringar av verktygets orientering, speciellt i avseende precision, repeterbarhet och tillförlitlighet.

Methods for Direction of Arrival, DOA estimation of multiple objects based on phased arrayantenna technology have many advantages in for example electronic warfare and radarapplications. However, perfect calibration of an antenna array can seldom be achieved. Thepurpose of this report is to study different methods for DOA estimation and how calibration-/modelerrors affect the results. Possible methods for quantifying these kinds of errors using measurement data are suggested. This thesis consists of essentially five parts. The different studies have been carried out using MATLAB simulations as well as theoretical considerations, i.e., calculations. In the first study, examples of the possible performance of four DOA algorithms, MUSIC, TLS-ESPRIT, WSF, and DML are provided. Results are given both with and without applying spatial smoothing. The latter scheme is used for handling correlated, or even coherent, sources. The results show that, for the considered scenarios, MUSIC performs the most consistently well, while the performance of DML is inferior. ESPRIT is well-performing when spatial smoothing is applied and performs the best when the angles of two signals are very close. It has been observed that WSF with weighting matrices for optimal asymptotic performance as well as spatial smoothing applied doesn’t perform well. When applying model errors to the systemin the second study, the corresponding conclusions about the algorithms can be drawn. That separation distance between the angles and that higher SNR results in better estimates are also confirmed. Quantification of certain array errors is also considered using methods inspired by a scheme proposed in the context of nonlinear system identification. The results show that the DOA algorithms are very good at dealing with noise and that the attempted method works well when the model error is like the true signals, but different enough that it is not confused with a problem with more signals. The model error that results in the worst results is when it only affects some ofthe channels in the antenna array. The fourth study explores DOA estimation using extended Kalman filtering and concludes that it is a very good tracker of the angle over time for the considered scenarios. All of this is then applied to measured data, but due to either extensive model error, errors with processing the data, or both, the results are worse than expected. Simulations that try to replicate the measured data results in good angle estimation for the DOA algorithms. The Kalman filter also performs well in simulations.

No description available.

Longline fishing is a method of fishing that utilizes baited hooks to catch fish in an environmentally friendly way. In order to reduce the number of catch lost while longline fishing, it is of great interest to be able to keep an even tension on the fishing line. This can be done by estimating the speed at the point of interest (POI) at which the fishing line is attached to the boat. Due to the harsh conditionson the seas, it is not recommended to put any sensors directly at that point. The aim of this thesis was to explore whether or not it is possible to estimate the vertical speed at the POI by having sensors measuring linear acceleration and angular velocity at various unknown places in the boat. The sensors were placed at various places in a simulated boat, after which the sensor orientations and positions were calculated using a nonlinear Least Squares method. After the sensors were positioned, an Extended Kalman Filter (EKF) was implemented on each sensor, after which the speed of the POI was calculated as the fused estimate of all EKFs. By changing the number of sensors and their sampling times, the best compromise between accuracy, computational load and number of sensors was found. The results prove that it is fully possible to estimate the vertical speed of the POI using only four 6DOF IMU:s using a sampling time of 50 or 100 ms, depending on how accurate the user wants the estimated positions of the sensors to be. However, there are still many ways in which the method used can be improved to geta better estimate.

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